This paper considers joint learning of multiple sparse Granger graphical
models to discover underlying common and differential Granger causality (GC)
structures across multiple time series. This can be applied to drawing
group-level brain connectivity inferences from a homogeneous group of subjects
or discovering network differences among groups of signals collected under
heterogeneous conditions. By recognizing that the GC of a single multivariate
time series can be characterized by common zeros of vector autoregressive (VAR)
lag coefficients, a group sparse prior is included in joint regularized
least-squares estimations of multiple VAR models. Group-norm regularizations
based on group- and fused-lasso penalties encourage a decomposition of multiple
networks into a common GC structure, with other remaining parts defined in
individual-specific networks. Prior information about sparseness and sparsity
patterns of desired GC networks are incorporated as relative weights, while a
non-convex group norm in the penalty is proposed to enhance the accuracy of
network estimation in low-sample settings. Extensive numerical results on
simulations illustrated our method's improvements over existing sparse
estimation approaches on GC network sparsity recovery. Our methods were also
applied to available resting-state fMRI time series from the ADHD-200 data sets
to learn the differences of causality mechanisms, called effective brain
connectivity, between adolescents with ADHD and typically developing children.
Our analysis revealed that parts of the causality differences between the two
groups often resided in the orbitofrontal region and areas associated with the
limbic system, which agreed with clinical findings and data-driven results in
previous studies.

本文介绍了一种基于稀疏学习的方法，通过多个 Granger 图对多个时间序列进行联合学习以发现共同和差异的 Granger 因果关系结构，该方法在 ADHD 的 fMRI 数据集中找到了两组个体的大脑连接机制及其差异。

通过非凸正则化共同学习多个格兰杰因果网络：推断群体级别的脑连接

Joint learning of multiple Granger causal networks via non-convex  regularizations: Inference of group-level brain connectivity

Penetration Testing is a methodology for assessing network security, by
generating and executing possible hacking attacks. Doing so automatically
allows for regular and systematic testing. A key question is how to generate
the attacks. This is naturally formulated as planning under uncertainty, i.e.,
under incomplete knowledge about the network configuration. Previous work uses
classical planning, and requires costly pre-processes reducing this uncertainty
by extensive application of scanning methods. By contrast, we herein model the
attack planning problem in terms of partially observable Markov decision
processes (POMDP). This allows to reason about the knowledge available, and to
intelligently employ scanning actions as part of the attack. As one would
expect, this accurate solution does not scale. We devise a method that relies
on POMDPs to find good attacks on individual machines, which are then composed
into an attack on the network as a whole. This decomposition exploits network
structure to the extent possible, making targeted approximations (only) where
needed. Evaluating this method on a suitably adapted industrial test suite, we
demonstrate its effectiveness in both runtime and solution quality.

采用部分可观测马尔科夫决策过程 (POMDP) 对攻击计划问题进行建模，以在网络结构允许的情况下进行分解，从而实现对单个机器的攻击，并将其组合成对整个网络的攻击，以提高针对性，缩减运行时间并提高解决方案质量。